# R/vimp_ci.R In vimp: Perform Inference on Algorithm-Agnostic Variable Importance

#### Documented in vimp_ci

#' Confidence intervals for variable importance
#'
#' Compute confidence intervals for the true variable importance parameter.
#'
#' @param est estimate of variable importance, e.g., from a call to \code{vimp_point_est}.
#' @param se estimate of the standard error of \code{est}, e.g., from a call to \code{vimp_se}.
#' @param scale scale to compute interval estimate on (defaults to "identity": compute Wald-type CI).
#' @param level confidence interval type (defaults to 0.95).
#'
#' @return The Wald-based confidence interval for the true importance of the given group of left-out covariates.
#'
#' @details See the paper by Williamson, Gilbert, Simon, and Carone for more
#' details on the mathematics behind this function and the definition of the parameter of interest.
#' @importFrom stats qlogis plogis
#' @export
vimp_ci <- function(est, se, scale = "identity", level = 0.95) {
# set up the level
a <- (1 - level)/2
a <- c(a, 1 - a)

# get the quantiles
fac <- stats::qnorm(a)

# create the ci
ci <- array(NA, dim = c(length(est), 2L), dimnames = list(names(est)))
# get scale
scales <- c("log", "logit", "identity")
full_scale <- scales[pmatch(scale, scales)]
if (full_scale == "log") {
tmp <- suppressWarnings(log(est))
is_zero <- FALSE
if ((is.na(tmp) & est <= 0 & !is.na(est))| is.infinite(tmp)) {
tmp <- 0
is_zero <- TRUE
}
ci[] <- exp(tmp + sqrt(se ^ 2 * grad ^ 2) %o% fac)
if (is_zero) {
ci[, 1] <- 0
}
} else if (full_scale == "logit") {
tmp <- suppressWarnings(qlogis(est))
is_zero <- FALSE
if ((is.na(tmp) & est <= 0 & !is.na(est)) | is.infinite(tmp)) {
tmp <- 0
is_zero <- TRUE
}
grad <- 1 / (est - est ^ 2)
ci[] <- plogis(tmp + sqrt(se ^ 2 * grad ^ 2) %o% fac)
if (is_zero) {
ci[, 1] <- 0
}
} else {
ci[] <- est + (se) %o% fac
if (any(ci[, 1] < 0) & !all(is.na(ci))) {
ci[ci[, 1] < 0, 1] <- 0
}
}

return(ci)
}

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vimp documentation built on Aug. 16, 2021, 5:08 p.m.